Therapeutic drugs (thereafter also referred to as “drug” or “drugs”) can be either natural products, or small molecule drugs, or peptides, or therapeutic proteins (biotherapeutics), or small-molecule-biotherapeutic conjugates (Barbosa, M. D. F. S. et al. 2002 Anal. Biochem. 306: 17-22; Barbosa, M. D. F. S. and Smith, D. D. 2014 Drug Discov. Today. 19: 1897-1912; Grunfeld, C. et al. 2011 Nat. Rev. Drug Discov. 10: 95-96; Kozlowski, S. et al. 2011 N. Engl. J. Med. 365: 385-388; Lee, S. et al. 2013 Nat. Biotechnol. 31: 220-226; Woodcock, J. et al. 2007 Nat. Rev. Drug Discov. 6: 437-442; all expressly incorporated by reference herein). Combination therapies (in which more than one molecular entity is used) are also common.
In attempts to improve efficacy and/or to protect intellectual property positions, several new versions of marketed therapeutic drugs have been developed. In some instances, the novelty consists of introducing mutations to existing drugs. For example, several new insulins are now available for treatment of diabetes, which contain mutated protein sequences relative to native insulin. Protein mutations may significantly alter the drug properties (including but not restricted to aggregation propensity), and may also create epitopes involved in T cell activation and anti-drug antibody (ADA) responses (Barbosa, M. D. F. S. et al. 2006. Clin. Immunol. 118: 42-50; expressly incorporated by reference herein). Unwanted immunogenicity is also a concern for biosimilar versions of marketed protein drugs, typically requiring postmarketing surveillance
Besides human genetics, many other factors may be involved in ADA responses against biotherapeutics, such as protein aggregation, sub-visible particles, route of administration, dose, glycosylation, amino acid composition of the protein (and the existence of protein epitopes that can bind to HLA molecules), impurities and others (Barbosa M. D. F. S. et al. 2012 Drug Discov. Today 17: 1282-1288; van Beers, M. M. C. et al. 2011 Pharm. Res. 28: 2393-2402; expressly incorporated by reference herein). Hence, biotherapeutics with identical amino acid sequences may trigger different host immune responses, which may also be dependent on the host genetic makeup (Barbosa, M. D. F. S. and Smith, D. D. 2014 Drug Discov. Today 19: 1897-1912; expressly incorporated by reference herein).
ADAs may impact safety and/or efficacy of therapeutic drugs. It should be noted that loss of efficacy due to ADAs can also be problematic for drug development. One such example is axokine, a modified form of ciliary neurotrophic factor that had been in development for obesity treatment. It was detected during phase 3 clinical trials that 70% of the patients developed anti-axokine ADAs, which decreased efficacy of the drug, ultimately leading to discontinuation of axokine development (Korner, J. and Aronne, L. J. 2004 J. Clin. Endocrinol. Metab. 89: 2616-2621; expressly incorporated by reference herein).
Hosts such as humans and test animals can also mount ADA responses against molecules other than therapeutic proteins. For example, anti-polyethylene glycol (anti-PEG) ADAs have been often observed when hosts are dosed with therapeutic drug-PEG conjugates (Barbosa, M. D. F. S. et al. 2013 Anal. Biochem. 441: 174-179; Judge, A. et al. 2006 Mol. Ther. 13: 328-337; Verhoef, J. F. et al. 2014 Drug Discov. Today 12: 1945-1952; all expressly incorporated by reference herein). Furthermore, the ADAs may be specific for drug degradation products.
A competent host immune system may mount unwanted responses to therapeutic drugs, such as the formation of neutralizing and/or non-neutralizing ADAs and/or various types of hypersensitivity (Barbosa, M. D. F. S. and Smith, D. D. 2014 Drug Discov. Today. 19: 1897-1912; expressly incorporated by reference herein). Host immune reactions often play an important role in adverse effects of therapeutic drugs. Various adverse reactions can result from the use of therapeutic drugs, for example life-threatening IgE- or IgG-mediated anaphylaxis or anaphylactic shock (Barbosa, M. D. F. S. and Smith, D. D. 2014 Drug Discov. Today. 19: 1897-1912; expressly incorporated by reference herein). Although immunogenicity may be associated with all drug classes, the main focus has been in immunogenicity of biologic drugs, likely due to their documented magnitude compared to immunogenicity of small molecules (Barbosa, M. D. F. S. and Smith, D. D. 2014 Drug Discov. Today. 19: 1897-1912; expressly incorporated by reference herein). ADAs may cause clinical syndromes ranging from mild hypersensitivity reactions to life-threatening responses, and may also decrease efficacy of the drug by directly neutralizing activity or by increasing drug clearance (Barbosa, M. D. F. S. and Smith, D. D. 2014 Drug Discov. Today. 19: 1897-1912; Rosenberg, A. S. 2003 Dev. Biol. [Basel] 112: 15-21; Woodcock, J. et al. 2007 Nat. Rev. Drug Discov. 6: 437-442; all expressly incorporated by reference herein).
Antibodies (also named immunoglobulins) are proteins that bind a specific antigen. In mammals such as humans and mice, antibodies contain paired heavy and light polypeptide chains. Standard antibody structural units typically comprise a tetramer. Each tetramer is usually composed of two identical pairs of polypeptide chains, each pair having one “light” (typically having a molecular weight of about 25 kDa) and one “heavy” chain (typically having a molecular weight of approximately 50 kDa). “Isotype” as used herein is meant any of the subclasses of immunoglobulins. The known human antibody isotypes are IgG1, IgG2, IgG3, IgG4, IgA1, IgA2, IgM1, IgM2, IgD, and IgE.
Each antibody chain contains a variable and a constant region, as described above. The variable regions of the light and heavy chains are required for binding the target molecule (the antigen). All ADAs are capable of binding to a target molecule, and hence are referred to as binding antibodies.
An ADA that blocks or diminishes activity of the target protein is designated as a neutralizing antibody, commonly abbreviated to NAb (Shankar, G. et al. 2014 AAPS J. 16: 658-673; expressly incorporated by reference herein). While some IgM can be neutralizing, usually most neutralizing ADAs (NAbs) are of the IgG type.
The following Igs are typically observed in higher mammals: IgD, IgA, IgE, IgM and IgG. IgD amounts to a small percentage of total serum Igs (less than 1%); IgA and IgM can comprise approximately 10-20%. IgG is the predominant Ig in blood. IgM is generally known as the early antibody, as it precedes the IgG response. A draft guidance document issued by the U.S. Food and Drug Administration (FDA) in 2009, recommends that ADA assays should be able to detect all isotypes, particularly immunoglobulin M (IgM) and the different immunoglobulin G (IgG) isotypes.
Host antibody responses against an antigen are typically polyclonal, comprising immunoglobulins that bind the antigen with various affinities and/or avidities. Hence, the assays used to detect antibody responses against therapeutic drugs are inherently qualitative, because there is no positive control antibody that would accurately represent all diverse antibodies in each of the samples collected from diverse sources and/or at various times following antigen exposure.
Product failure greatly increases the cost of developing new drugs, as the industry may incorporate the cost of many drugs that fail during development into the high costs of the few drugs that are approved. Hence, there is considerable interest in predicting and mitigating immunogenicity of biotherapeutics during preclinical discovery and development. Therefore, several attempts have been made to predict immunogenicity of therapeutic proteins during discovery and preclinical development, in particular predictions based on the protein T cell epitope content (“T cell epitope” here defined as amino acid sequences capable of binding to MHC molecules).
Despite those efforts to predict immune responses based on the protein T cell epitope content, it is not possible to ascertain in vitro and without clinical data how the drug will perform in humans. In fact, there are known examples of preclinical immunogenicity predictions that were not confirmed with clinical data (Stickler, M. et al. 2004 Genes Immun. 5: 1-7; Barbosa, M. D. F. S. et al. 2006 Clin. Immunol. 118: 42-50; Barbosa, M. D. F. S. and Celis, E. 2007 Drug Discov. Today. 12: 674-681; Barbosa, M. D. F. S. 2011 Drug Discov. Today 16: 345-353; all expressly incorporated by reference herein). Attempts to predict protein immunogenicity in the absence of clinical data based on protein MHC epitope content may be misleading, as in vivo tolerance and immunogenicity mechanisms may share similar determinants (Barbosa, M. D. F. S. and Celis, E. 2007 Drug Discov. Today 12: 674-681; Couzin, J. 2004 Science 305: 772-; Chaudhry, A. et al. 2009 Science 326: 986-991; Munn, D. H. et al. 2002 Science 297: 1867-1870; Pan, F. et al. 2009 Science 325: 1142-1146; all expressly incorporated by reference herein).
A much more reliable estimation of the likelihood of immune reactions against a therapeutic protein can be based on methods resulting from the composition of clinical data, which can be used for example for statistical analyses of associations between ADA responses and other factors. In order to incorporate a human data-driven approach to immunogenicity prediction and mitigation, methods and processes are needed to systematically harvest and utilize clinical data. Such methods can also be used in conjunction with non-clinical data, and are within the scope of the present invention.
The major histocompatibility gene complex (MHC) is a group of genes that code for proteins involved in immune recognition of foreign substances. In humans, the MHC complex is also named the human leukocyte antigen (HLA) system. The two main types of MHC proteins are MHC class I and MHC class II, and they are very polymorphic cell-surface molecules. The T cell receptor (TCR) recognizes peptides bound to MHC I or MHC II molecules. Antibodies are produced by activated B cells that proliferate and differentiate into antibody producing plasma cells. B cell activation can be dependent or not of T cells. T-independent antigens activate B cells without the need for T-cell help (Zeng, M. et al. 2014 Science 346: 1486-1492). T-dependent antigens are taken up by antigen processing cells, processed and presented (bound to MHC class II molecules) to helper T cells which are involved in B cell activation (Barbosa, M. D. F. S. 2011 Drug Discov. Today 16: 345-353). The human gene complex coding for MHC class II proteins includes three loci (DR, DQ and DP), each containing genes coding for the alpha and beta subunits of an MHC molecule. Following uptake and processing of a protein by antigen presenting cells (for example, dendritic cells), antigenic peptides bound to MHC class II molecules are presented at the cell surface. The peptide bound to the MHC protein forms a complex with a T cell receptor, causing activation of T cells, and ultimately antibody production by differentiated B-cells. That process is also dependent on interactions between co-stimulatory molecules, for example CD28 and CD80/CD86. MHC proteins can vary in their antigen binding specificities; hence, depending on their HLA type, individuals may respond differently to the same antigen (Barbosa, M. D. F. S. and Celis, E. 2007 Drug Discov. Today 12: 674-68; expressly incorporated by reference herein).
Isotype switching and the IgG response are generally T cell dependent, and hence can be associated with specific Human Leukocyte Antigen (HLA) types (Barbosa, M. D. F. S. et al. 2006 Clin. Immunol. 118: 42-50; Barbosa, M. D. F. S. and Celis, E. 2007 Drug Discov. Today. 12: 674-681; Barbosa, M. D. F. S. 2011 Drug Discov. Today 16: 345-353; all expressly incorporated by reference herein). For example, by testing plasma and genetic material of patients treated with Betaseron® (an interferon-β therapeutic protein), it was shown that the major histocompatibility complex (MHC) class II allele DRB1*0701 is associated with anti-interferon-β (anti-IFN-β) ADAs of the IgG type (Barbosa, M. D. F. S. et al. 2006 Clin. Immunol. 118: 42-50; expressly incorporated by reference herein). Even when patients treated with three different IFN-β formulations were evaluated in a large clinical study, strong associations were observed between HLA types (HLA-DRB1*0401 and HLA-DRB1*0408) and ADAs (Hoffmann, S. et al. 2008 Am. J. Hum. Gen. 83: 219-227; expressly incorporated by reference herein). HLA class II binding epitopes can thus activate T helper cells leading to immune responses (Barbosa, M. D. F. S. et al. 2006 Clin. Immunol. 118: 42-50; Barbosa, M. D. F. S. and Celis, E. 2007 Drug Discov. Today 12: 674-681; Tatarewicz, S. M. et al. 2007 J. Clin. Immunol. 27: 620-627; Dalum, I. et al. 1997 Mol. Immunol. 34: 1113-1120; all expressly incorporated by reference herein). It should be noted that in some instances protein epitopes may activate regulatory T cells, which are involved in self-tolerance (Barbosa, M. D. F. S. and Celis, E. 2007 Drug Discov. Today 12: 674-681; De Groot, A. S. et al. 2008 Blood 112: 3303-3311; all expressly incorporated by reference herein). Genetic components other than HLA types may also be immunogenicity determinants (Magdaleine-Beuzelin, C. et al. 2009. Pharmacogenet. Genomics 19: 383-387; Tatarewicz, S. M. et al. 2012. J. Immunol. Methods 382: 93-100; expressly incorporated by reference herein).
In another embodiment, analyses and data in the databases of the present invent can allow determinations of modifications leading to host tolerance to protein drugs (i.e., absence of host immune responses against the drug).
HLA typing of patients treated with Betaseron® and analysis of genetic associations with ADAs has been performed (Barbosa et al, 2006 “Clinical link between MHC class II haplotype and interferon-β (IFN-β) immunogenicity” Clin. Immunol. 118: 42-50; expressly incorporated by reference herein), but to date this has not been a common procedure. Challenges associated with implementation of those association analyses included lack of standardization of ADA assays to be used for identifying genetic associations with ADA responses across products. It should be noted that excluding patients likely to mount ADA responses against a given biotherapeutic might decrease industry profits for that particular therapeutic drug. Hence, in some instances economic drivers may not favor immunogenicity risk assessment if performed by the same industry which profits from the biotherapeutic in question (Barbosa, M. D. F. S. and Smith, D. D. 2014 Drug Discov. Today 19: 1897-1912; expressly incorporated by reference herein).
Another difficulty associated with monitoring ADAs for approved products is the cumbersome nature of collecting patient blood and shipping samples (commonly plasma or serum after blood processing) under special conditions to labs approved for such testing, and the lack of unified methodologies at such laboratories. In addition, such procedures are expensive and time-consuming, and in many instances laboratories offering those services are not even available and/or not known to physicians and/or patients. What follows is that there is an unmet need for available methods and devices to readily detect ADAs and to perform risk assessment for biotherapeutics. Such systems and methods can have several utilities, including but not restricted to stratification of patients likely to benefit from a given therapy, comparison of similar products marketed for the same indication, guidance for new product development, tests during clinical trials, and postmarketing surveillance. Currently it is common practice to indicate in the label of approved biotherapeutics that it would be misleading to compare immunogenicity data with other products, due to differences in assays, and lack of standardization of sample handling and collection and ADA detection for different therapeutic drugs. In addition, specifics of the assays used to detect ADA during clinical trials are typically not disclosed in the product labels (Barbosa, M. D. F. S. and Smith, D. D. 2014 Drug Discov. Today 19:1897-1912; Expressly incorporated by reference herein). Those challenges are becoming increasingly complex, as a growing number of products are approved by regulatory agencies, including but not restricted to biosimilars (a biosimilar is a biotherapeutic similar to another one already marketed for which the patent has expired) and modified versions of marketed biotherapeutics. Although monitoring ADAs is typically a regulatory requirement for development and approval of protein drugs, it is difficult to unify testing procedures for all the drugs approved. During the development of a biosimilar, the same assay may be used to test ADA for comparison of the biosimilar with the reference product, although often there is no systematic postmarketing testing of the reference product. Importantly, in other situations there have been no mechanisms in place to compare systematically therapeutic drugs approved for the same application regarding their immunogenicity in humans, and one of the difficulties is that the assays used vary from one product to the other (Barbosa, M. D. F. S. and Smith, D. D. 2014 Drug Discov. Today 19: 1897-1912; expressly incorporated by reference herein).
The difficulties associated with implementation of current approaches to postmarketing assessment of therapeutic drugs has been reviewed (Barbosa, M. D. F. S. and Smith, D. D. 2014 Drug Discov. Today 19: 1897-1912; expressly incorporated by reference herein). It can be anticipated that the need for comparing traditional drugs with gene therapy will further add to those challenges (Gaudet, D. et al. 2012 Curr. Opin. Lipidol. 23: 310-320; Bennett, J. et al. 2012. Sci. Transl. Med. 4: 120ra115; Nathwani, A. C. et al. 2011 N. Engl. J. Med. 365: 2357-2365; Wang, J. et al. 2008 Nat. Biotechnol. 26: 901-908; Banugaria, S. G. et al. 2011 Genet. Med. 13: 729-736; all expressly incorporated by reference herein). The US Food and Drug Administration has recently initiated an active surveillance system (“the Sentinel Initiative”), which has been defined by the Brookings Institution as “a national, integrated, electronic system for active surveillance of medical product safety that utilizes the capabilities of multiple, existing data systems” (Behrman, R. E. et al. 2011 N. Eng. J. Med. 364: 498-499; Platt, R. et al. 2012 Pharmacoepidemiol. Drug Saf. 21[Suppl. 1]: 1-8; Platt, R. et al. 2009 N. Eng. J. Med. 361: 645-647; all expressly incorporated by reference herein). However, one of the challenges associated with some aspects of drug comparisons with that system is that, in many cases, various different assays are used, resulting in data that is not amenable to the computational analysis.
Pre-existing ADAs (which are present in patients prior to their dosing with a therapeutic drug) may be a risk factor for the development of NAbs (Barbosa, M. D. F. S. and Smith, D. D. 2014 Drug Discov. Today 19: 1897-1912; expressly incorporated by reference herein). An anonymized database containing HLA types and their associations with ADA responses, and also other genetic information, can be used in connection with ADA detection to predict risk of ADA development, as described in embodiments of this invention. In other words, whether pre-existing ADAs are detected or not, the physician and/or patient can access a database for information with anonymized genetic information. Either one of those factors (ADAs or genetics) could indicate risk, with a combination of factors indicating even greater risk.
With a plethora of therapeutic drugs being approved for the same indication, it is becoming increasingly complex for physicians and patients to select the medication likely to provide most benefits (Downing, N. S. et al. 2014 JAMA. 311: 368-377; expressly incorporated by reference herein). For instance, several formulations of interferon-β (IFN-β) are marketed (Rebif®, Betaseron®/Betaferon®, Avonex®, and Pelegridy®), and recently IFN-β biosimilars are also being approved (Barbosa, M. D. F. S. and Smith, D. D. 2014 Drug Discov. Today 19: 1897-1912; expressly incorporated by reference herein). Readily available methods and devices to detect ADAs and perform risk assessment can allow effective comparison of similar products marketed for the same indication, or to compare different products regarding suitability for specific patients.
Establishing the correct dosing of a therapeutic drug that would elicit optimal benefits with acceptable safety profile is also challenging, and selection of dose to be administered to patients is often done during phase 1 clinical trials (dose escalation studies), with a limited number of human subjects. Being able to use the minimal amount of drug that enable the benefits sought is highly desirable, both from the perspective of patient safety and healthcare costs. Many treatments are very expensive (including but not restricted to enzyme replacement therapies and cancer therapeutics), with the drug price often established for mg amounts of the drug. In cases when the patients have pre-existing antibodies against the test therapeutic drug, a higher dose may be required to compensate for the NAb effect; hence, methods and compositions to readily screen patients are also highly desirable from a dose selection perspective (Barbosa, M. D. F. S. and Smith, D. D. 2014 Drug Discov. Today. 19: 1897-1912; expressly incorporated by reference herein).
ADA incidence against chronically administered products such as insulin and enzyme replacement therapies is also a concern. Even if the drug dosage is increased to compensate for NAbs, the chronic administration may results in immune complexes not being cleared, leading to immune complex disease and/or other syndromes (Barbosa, M. D. F. S. and Smith, D. D. 2014 Drug Discov. Today 19: 1897-1912; expressly incorporated by reference herein). In such cases, knowledge of ADA incidence and monitoring can provide an effective mechanism to evaluate risk and the need for tolerance induction regimens (Messinger, Y. H. et al. 2012 Genet. Med. 14: 135-142; expressly incorporated by reference herein). This can be further complemented by knowledge of associated genetic components. Methods to assess risk of immune responses can also be useful to guide therapies other than the ones requiring chronic administration (Ritter, G. et al. 2001 Cancer Res. 61: 6851-6859; expressly incorporated by reference herein).
Although the relevance of preventing and monitoring undesirable human immune reactions against therapeutic drugs has been widely recognized, in many cases processes and methods to systematically address those issues are lacking. For example, despite numerous attempts to standardize guidelines for determination of neutralizing and non-neutralizing ADAs (Mire-Sluis, A. R. et al. 2004 J. Immunol. Methods. 289: 1-16; Shankar, G. et al. 2008 J. Pharm. Biomed. Anal. 48: 1267-1281; Gupta, S. et al. 2007 J. Immunol. Methods 321: 1-18; Gupta, S. et al. 2011 J. Pharm. Biomed. Anal. 55: 878-888; Koren, E. at al. 2008 J. Immunol. Methods 333: 1-9; Shankar, G. et al. 2014 AAPS J. 16: 658-673; Barbosa, M. D. F. S. et al. 2012 J. Immunol. Methods 384:152-156; all expressly incorporated by reference herein), methods used to test ADAs for similar therapeutic drugs have varied widely, resulting in discrepant results and/or inability to compare products regarding their immunogenicity profile (Barbosa, M. D. F. S. et al., 2012 Drug Dscov. Today 17: 1282-1288; Barbosa, M. D. F. S. and Smith, D. D. 2014 Drug Discov. Today 19: 1897-1912; all expressly incorporated by reference herein). One of the major problems is that assay formats differ from one product to the next.
The present invention provides methods and compositions for evaluation of immunogenicity risk and/or immune responses against therapeutic drugs. Such methods and processes can be used to stratify patients prior to therapy, and/or to monitor efficacy and/or safety of therapy, and/or to guide discovery of novel therapeutic entities, and/or to guide therapeutic drug development, and/or to estimate possibility of adverse events, and/or to compare therapeutic drugs, and/or to estimate need for tolerance induction, and/or to empower doctors and patients regarding treatment decisions, and/or for postmarketing surveillance.